Network Characteristics and Supply Chain Resilience Under Risk Propagation: Bayesian Network and Agent-Based Approach

This study examines how the structural characteristics of networks influence supply chain resilience under risk propagation. The study developed a probabilistic framework that integrates Bayesian networks with agent-based modeling. In this framework, recovery is modeled as a second propagation process that mirrors the disruption. Disruption cascades downstream, while restoration support flows upstream from customers. Three phase-specific metrics—probabilistic network robustness at onset, supply network structural recoverability at the peak, and a recoverability measure over the recovery window—are evaluated across 16,200 simulations spanning random, scale-free, small-world, and hybrid networks. Although the four topologies are statistically indistinguishable across all three metrics, regression analysis reveals systematic associations between structural characteristics and resilience that topology-level comparison alone obscures. These associations shift across resilience phases. Betweenness centrality is positively associated with all three metrics. Hub dominance shows its strongest positive association with initial robustness, and the alternative path index is negatively associated with PNR but positively associated with SNSR and RM. Disabling customer support halves realized recovery, confirming the substantive contribution of recovery propagation to overall resilience. Building on these findings, this study proposes an integrated classification framework comprising strategic and operational matrices that translates structural characteristics into actionable resilience management strategies.